Machine Learning Engineer; on-site

Overview

Project description

We are seeking a skilled Machine Learning Engineer to develop and deploy Graph Neural Network (GNN) based surrogate models that approximate complex physics simulations for oil & gas pipeline and well networks. This is a hands-on role for someone who can build high-fidelity neural network models that replace computationally expensive reservoir and network simulators (Nexus, Prosper).

• Design and implement Neural Network architectures to model flow dynamics in interconnected pipeline networks
• Build surrogate models that accurately predict pressure distributions, flow rates, and network behavior under varying operational scenarios (training data is acquired through running simulations of the physics models)
• Create data pipelines to extract network topology and simulation results from physics-based models (Nexus/Prosper) and transform them into graph representations
• Develop training frameworks that incorporate physics constraints (conservation laws, pressure-flow relationships) into neural network loss functions
• Collaborate with petroleum engineers to ensure model predictions align with physical behavior and operational constraints
• Implement model monitoring, validation, and continuous improvement workflows
• Business trip to Kuwait for first 6-12 months. On-site

Must have

• Strong expertise in Graph Neural Networks (GCN, Graph
  
  SAGE, Message Passing Networks) with proven implementation experience
• Deep understanding of deep learning frameworks (PyTorch Geometric, DGL, or Tensor Flow GNN)
• Experience building surrogate models or physics-informed neural networks (PINNs) for engineering applications
• Proficiency in Python and scientific computing libraries (Num Py, Sci Py, Pandas)
• Demonstrated ability to work with complex data structures (graphs, time-series, spatial data)
• Understanding of optimization techniques and handling large-scale training data
• Understanding of graph theory and network analysis
• Experience with data structures and graph representations (adjacency matrices, edge lists, sparse tensors)
• Knowledge of hyperparameter tuning, model building and uncertainty quantification in ML models
• Ready for a long term business trip to Kuwait for first 6-12 months

Background in petroleum engineering, process engineering, or fluid dynamics;
Familiarity with reservoir simulation or pipeline hydraulics;
Experience with MLOps practices and model lifecycle management;
Publications or open-source contributions in graph ML;
Experience deploying ML models in production cloud environments (containerization, API development);
Industry experience in Oil & Gas is a strong plus, however candidates with relevant surrogate modeling experience from other engineering domains are encouraged to apply.

Educational Background: MS/PhD in Computer Science, Computational Engineering, Applied Mathematics, or related field preferred;
Strong mathematical foundation in linear algebra, graph theory, and numerical methods;
Understanding of graph theory and network analysis